Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation
Improves low-velocity state estimation for driver-assistance functions in electric vehicles, addressing sensor limitations.
A neural-network-based virtual wheel-speed sensor fuses wheel-speed and motor-speed signals to reduce errors at low velocities, achieving up to 85% error reduction over production sensors and 47% over optimized filters on real-world Volkswagen ID.7 data.
Accurate wheel speed information is crucial for vehicle control and state estimation. Conventional sensors suffer from quantization and latency, especially at low velocities, while motor-speed signals in electric vehicles are distorted by drivetrain torsion. This work presents a neural-network-based virtual wheel-speed sensor that fuses wheel-speed and motor-speed signals to reduce errors from both sources. Validated on real-world Volkswagen ID.7 data, the real-time capable model achieves an error reduction of up to 85% compared to the production sensor and 47% compared to an optimized zero-phase filter, providing a smooth signal for driver-assistance functions. The results demonstrate robust generalization across diverse real-world maneuvers within the vehicle platform.